Chonho Lee

Auction Approaches for Resource Allocation in Wireless Systems: A Survey

As wireless systems evolve with new mobile technologies, they tend to become complicated in terms of architectures and managements. Auction theory, as a subfield of economics and business management, has been introduced to provide an interdisciplinary technology for radio resource allocation (e.g., subchannels, time slots, and transmit power levels) in the wireless systems. By using various auction approaches, such radio resources are efficiently allocated among users and providers of services in the systems. Participants (i.e., users and providers) of an auction have their own strategies that follow the incentives and rules brought by the auction. Auction methods are widely employed in areas such as cognitive radio, cellular networks, and wireless mesh networks. This paper gives a comprehensive survey of recent auction approaches (i.e., auction-based applications and mechanisms) applied in wireless and mobile systems. First, auction theory and different types of auction are introduced. The motivation of using auction in wireless systems is given. Then, the reviews of auction approaches applied in the single-hop and multi-hop wireless networks are provided. Finally, the open research issues are discussed.

A Real-Time Group Auction System for Efficient Allocation of Cloud Internet Applications

The increasing number of cloud-based Internet applications has led to the demand for efficient resource and cost management. This paper proposes a real-time group auction system for the cloud instance market. The system is designed based on a combinatorial double auction, and its applicability and effectiveness are evaluated in terms of resource efficiency and monetary benefits to auction participants (e.g., cloud users and providers). The proposed auction system helps them decide when and how providers will allocate their resources and to which users. Furthermore, we propose a distributed algorithm using a group formation game that determines which users and providers will trade resources by their cooperative decisions. To find how to allocate the resources, the utility optimization problem is formulated as a binary integer programming problem and the nearly optimal solution is obtained by a heuristic algorithm with quadratic time complexity. In comparison studies, the proposed real-time group auction system with cooperation outperforms an individual auction in terms of the resource efficiency (e.g., the request acceptance rate for users and resource utilization for providers) and monetary benefits (e.g., average payments for users and total profits for providers).

An evolutionary game theoretic approach to adaptive and stable application deployment in clouds

This paper studies an evolutionary game theoretic mechanism for adaptive and stable application deployment in cloud computing environments. The proposed mechanism, called Nuage, allows applications to adapt their locations and resource allocation to the environmental conditions in a cloud (e.g., workload and resource availability) with respect to given performance objectives such as response time. Moreover, Nuage theoretically guarantees that every application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given environmental conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies.

SWAT: A Decentralized Self-Healing Mechanism for Wormhole Attacks in Wireless Sensor Networks

This paper proposes and evaluates a decentralized self-healing mechanism that detects and recovers from wormhole attacks in wireless multi-hop sensor networks. Upon detecting a wormhole attack, the proposed mechanism, called SWAT, identifies the locations of malicious nodes (or wormhole nodes), isolates them from the network and recovers the routing structure distorted by them. SWAT is the first mechanism that performs both wormhole node isolation and routing structure recovery against wormhole attacks. Unlike many other wormhole detection mechanisms, SWAT does not require any extra networking facilities (e.g., timing analysis and localization facilities) as well as special hardware (e.g., GPS). Instead, it uses network connectivity information only in a decentralized manner. Simulation results show that SWAT yields 100% wormhole attack detection, 0% false detection, 100% wormhole node isolation and 0% false isolation in dense networks. The results also show that SWAT outperforms multi-path routing mechanisms in terms of control overhead and power consumption and outperforms another connectivity-based detection mechanism in terms of false isolation rate and recovery efficiency.

Towards a Biologically-inspired Architecture for Self-Regulatory and Evolvable Network Applications

Summary The BEYOND architecture applies biological principles and mechanisms to design network applications that autonomously adapt to dynamic environmental changes in the network. In BEYOND, each network application consists of distributed software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as energy exchange, migration, reproduction and replication. This paper describes two key components in BEYOND: (1) a self-regulatory and evolutionary adaptation mechanism for agents, called iNet, and (2) an agent development environment, called BEYONDwork. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. In iNet, a configuration of antibodies is encoded as a gene. Agents evolve their antibodies so that they can adapt to unexpected environmental changes. iNet also allows each agent to detect its own deficiencies to detect antigen invasions (i.e., environmental changes) and regulate its policy for antigen detection. Simulation results show that agents adapt to changing network environments by self-regulating their antigen detection and evolving their antibodies through generations. BEYONDwork provides visual and textual languages to design agents in an intuitive manner.

An immunologically-inspired autonomic framework for self-organizing and evolvable network applications

Network applications are increasingly required to be autonomous, scalable, adaptive to dynamic changes in the network, and survivable against partial system failures. Based on the observation that various biological systems have already satisfied these requirements, this article proposes and evaluates a biologically-inspired framework that makes network applications to be autonomous, scalable, adaptive, and survivable. With the proposed framework, called iNet, each network application is designed as a decentralized group of software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as reproduction, migration, energy exchange, and death. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces specific antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (an antigen) to evaluate whether it adapts well to the sensed environment, and if it does not, adaptively perform a behavior (an antibody) suitable for the environment conditions. In iNet, a configuration of antibodies is encoded as a set of genes, and antibodies evolve via genetic operations such as crossover and mutation. Empirical measurement results show that iNet is lightweight enough. Simulation results show that agents adapt to dynamic and heterogeneous network environments by evolving their antibodies across generations. The results also show that iNet allows agents to scale to workload volume and network size and to survive partial link failures in the network.

Biologically-Inspired Design of Autonomous and Adaptive Grid Services

This paper describes and evaluates a biologically-inspired network architecture that allows grid services to autonomously adapt to dynamic environment changes in the network. Based on the observation that the immune system has elegantly achieved autonomous adaptation, the proposed mechanism, the iNet artificial immune system, is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and specifically reacts to them. iNet models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and a behavior of grid services (e.g., migration and replication) as an antibody. iNet allows each grid service to autonomously sense its surrounding environment conditions (an antigen) to evaluate whether it adapts well to the sensed conditions, and if it does not, adaptively perform a behavior (an antibody) suitable for the sensed conditions. Simulation results show that iNet allows grid services to autonomously adapt their population and location to environmental changes for improving their performance (e.g., response time and throughput) and balancing workload

An autonomic adaptation mechanism for decentralized grid applications

This paper describes and empirically evaluates a biologically-inspired adaptation mechanism that allows grid network services to autonomously adapt to dynamic environment changes in the network. Based on the observation that the immune system has elegantly achieved autonomous adaptation, the proposed mechanism, the iNet artificial immune system, is designed after the mechanisms behind how the immune system detects antigens (e.g. viruses) and specifically reacts to them. iNet models a set of environment conditions (e.g. network traffic and resource availability) as an antigen and a behavior of grid services (e.g. migration and replication) as an antibody. iNet allows each grid service to autonomously sense its surrounding environment conditions (i.e. an antigen) to evaluate whether it adapts well to the sensed conditions, and if it does not, adaptively perform a behavior (i.e. an antibody) suitable for the sensed conditions. Empirical evaluation results show that iNet works efficiently in acceptable degree of accuracy and makes grid services adaptive to dynamic network environment.

iNet-EGT: An Evolutionarily Stable Adaptation Framework for Network Applications

This paper studies a bio-inspired framework, iNet-EGT, to build autonomous adaptive network applications. In iNet-EGT, each application is designed as a set of agents, each of which provides a functional service and possesses biological behaviors such as migration, replication and death. iNet-EGT implements an adaptive behavior selection mechanism for agents. It is designed after an immune process that produces specific antibodies to antigens (e.g., viruses) for eliminating them. iNet-EGT models a set of network conditions (e.g., workload and resource availability) as an antigen and an agent behavior as an antibody. iNet-EGT allows each agent to autonomously sense its surrounding network conditions (an antigen) and select a behavior (an antibody) according to the conditions. This behavior selection process is modeled as a series of evolutionary games among behaviors. It is theoretically proved to converge to an evolutionarily stable (ES) equilibrium; a specific (i.e., ES) behavior is always selected as the most rational behavior against a particular set of network conditions. This means that iNet-EGT allows every agent to always perform behaviors in a rational and adaptive manner. Simulation results verify this; agents invoke rational (i.e., ES) behaviors and adapt their performance to dynamic network conditions.

GazeTry: Swipe Text Typing Using Gaze

Over the last decades, eye gaze has become an alternative form of text entry by some physically challenged people. Recently a dwell-free system has been proposed, which has been proven to be much faster compared to other existing dwell-free systems. However, it is vulnerable to some common text entry problems. In the paper, we propose GazeTry, a dwell-free gaze-based text entry system which allows people to type a word by gazing sequentially at the letters of the word. Simulation and experiments results show that our proposed new dwell-free system, GazeTry with the Moving Window String Matching (MoWing) algorithm has better accuracy and more resilience to text entry errors.